Data Screening Algorithm of Power Grid Security Hidden Danger Based on Edge Computing

Aiming at the problem that the power grid security hidden danger data investigation is interfered by the power grid attack path, which leads to the poor accuracy and low efficiency of the security hidden danger data investigation, an edge computing-based power grid security hidden danger data investigation algorithm is proposed. Combined with the quantitative regression analysis method, the ambiguity function of the power grid security situation awareness assessment is established, the power grid security situation is analyzed, the characteristics of the power grid security hidden danger data samples are extracted, and the power grid security hidden danger data analysis model is constructed based on the support vector machine. Based on the collaborative work of edge computing center nodes and edge nodes, the distributed data detection of power grid security is realized. The power grid security hidden danger data is reconstructed by the joint sparse model, the power grid security hidden danger data is classified, the power grid security hidden danger data feature set is calculated in the high-dimensional space, and the power grid security hidden danger data is checked through the edge detection. The experimental results show that the method has high accuracy and good throughput for the data investigation of power grid security risks, and can efficiently and accurately realize the data investigation of power grid security risks.